import os
from netrc import netrc

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

import tensorflow as tf
from tensorflow.keras import datasets, layers, optimizers, Sequential, metrics
from tensorflow import keras


def preprocess(x, y):
    x = tf.cast(x, dtype=tf.float32) / 255
    y = tf.cast(y, dtype=tf.int32)
    return x, y

batchsize = 128

# [50k, 32, 32, 3], [10k, 1]
(x, y), (x_val, y_val) = datasets.cifar10.load_data()
y = tf.squeeze(y)  # 移除大小为1的维度
y_val = tf.squeeze(y_val)

y = tf.one_hot(y, depth=10)  # [50k, 10]
y_val = tf.one_hot(y_val, depth=10)  # [10k, 10]
print('datasets:', x.shape, y.shape, x_val.shape, y_val.shape, x.min(), x.max())

train_db = tf.data.Dataset.from_tensor_slices((x, y))
train_db = train_db.map(preprocess).shuffle(10000).batch(batchsize)
test_db = tf.data.Dataset.from_tensor_slices((x_val, y_val))
test_db = test_db.map(preprocess).batch(batchsize)

sample = next(iter(train_db))
print('batch:', sample[0].shape, sample[1].shape)


class MyDense(layers.Layer):

    def __init__(self, inp_dim, outp_dim):
        super(MyDense, self).__init__()

        self.kernel = self.add_weight('w', [inp_dim, outp_dim])
        # self.bias = self.add_weight('b', [outp_dim])

    def call(self, inputs, training=None):
        x = inputs @ self.kernel
        return x


class MyNetwork(keras.Model):

    def __init__(self):
        super(MyNetwork, self).__init__()

        self.fc1 = MyDense(32*32*3, 256)
        self.fc2 = MyDense(256, 128)
        self.fc3 = MyDense(128, 64)
        self.fc4 = MyDense(64, 32)
        self.fc5 = MyDense(32, 10)

    def call(self, inputs, training=None):
        x = tf.reshape(inputs, [-1, 32*32*3])
        # [b, 32*32*3] -> [b, 256]
        x = self.fc1(x)
        x = tf.nn.relu(x)
        # [b, 256] -> [b, 128]
        x = self.fc2(x)
        x = tf.nn.relu(x)
        # [b, 128] -> [b, 64]
        x = self.fc3(x)
        x = tf.nn.relu(x)
        # [b, 64] -> [b, 32]
        x = self.fc4(x)
        x = tf.nn.relu(x)
        # [b, 32] -> [b, 10]
        x = self.fc5(x)

        return x


network = MyNetwork()
network.compile(optimizer=optimizers.Adam(lr=1e-3),
                loss=tf.losses.CategoricalCrossentropy(from_logits=True),
                metrics=['accuracy'])
network.fit(train_db, epochs=15, validation_data=test_db, validation_freq=1)

network.evaluate(test_db)
# network.save_weights('tf-07/weights.ckpt')
# del network
# print('saved to tf-07/weights.ckpt')
#
#
# network = MyNetwork()
# network.compile(optimizer=optimizers.Adam(lr=1e-3),
#                 loss=tf.losses.CategoricalCrossentropy(from_logits=True),
#                 metrics=['accuracy'])
# network.load_weights('tf-07/weights.ckpt')
# print('loaded weights from file.')
# network.evaluate(test_db)